Supervised Training

Supervised training is a machine learning technique where an algorithm learns from labeled data to make predictions or decisions on new, unseen data. In supervised training, the algorithm is trained on a dataset where the input data is labeled with the correct output. The algorithm learns to map the input to the output by minimizing the difference between its predicted output and the correct output. The goal of supervised training is to create a model that can accurately predict the output for new, unseen input data. Supervised training is commonly used in applications such as image recognition, speech recognition, and natural language processing.
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